Prescriptive Analysis: Recommending next actions

Prescriptive analysis uses a broad range of methods from machine learning and artificial intelligence to estimate the most likely outcomes and what actions to take next.

Questions usually answered are related to make buying recommendations to optimize supply chain inventory. Another good example of such applications is to recommend actions a user should take, like a virtual coach would do.

Behavioral Science: Understanding and Influencing Humans

Behavioral Science is the study of human behavior and includes sciences such as psychology, social neuroscience, and cognitive science.

It is useful to help us inform change management and improve adoption of data science results within a business. It is also useful to improve user experience, drive behavior change for better outcomes (for example in healthcare)…

Why do Data Science projects fail?

Data Science Challenges

Risk of Projects

While moonshot projects are necessary for your company to be relevant in the future, they are riskier than incremental projects and tend to fail more often. However, both are important components of a diversified portfolio.
Additionally, the research component of data science projects makes their outcome uncertain. This is a key difference from IT projects. They therefore demand different project management techniques to identify and manage a portfolio of projects, diversified in terms of risk factors like time horizon and business functions impacted.

Human Factors

In August 2017, Kaggle surveyed its users who identified several challenges faced by data scientists. Coordination and collaboration issues ranked number one, and highlighted the challenges between the different stakeholders: business, data science and IT.

Data

Different aspects of data are usually considered challenging: quality, access and privacy, relevance to the problem at hand, and data size.
The bigger the datasets, the more challenging it is to integrate complex technology stacks into existing business processes: using the right amount of data for the problem at hand is crucial. Bigger is not always better.
Another issue is the view that you can just run algorithms on large datasets and they will find the answer. Do not forget the Science part in data science: evidence-based approach as opposed to the purely data-driven approach.

Talent

The lack of data science talent is often mentioned as a key challenge. The skill set necessary to be a good data scientist is broad and varied. Since the discipline is relatively new, there are few people who have these skills.

Budget

The cost of hiring the right talent and buying the necessary technology platforms can be overwhelming. Acquiring the right data (some of which is external and expensive) may also require a budget that many firms cannot afford.

HOW ARE WE SOLVING IT?

STRATEGY

We help you define a data science strategy, optimized for the unique needs of your business

EXECUTION

Domain Expertise

We have expertise in several domains: Finance, Insurance, and Healthcare.

MODELING

We understand what types of models to use in order to solve a particular problem

PROGRAMMING

We interface with IT for a fast integration into production systems

Behavioral Science

Understanding human behavior is helpful to drive adoption of our work

We optimize your Data Science ROI

We optimize your return on investment by improving the success rate of your data science efforts. We accomplish this by a better risk management of your portfolio of projects, by reducing the human inefficiencies that threaten the success of those projects, by hiring and growing a talented in-house team, and by offering these services with a flexible cost structure that you can tailor to your budget. In doing so we overcome the main reasons why data science projects fail.

Firstly, we choose data science projects for their potential impact on the company’s bottom line, and how well they complement each other. Diversification along axes like complexity, time horizon, and business lines is a must. This results in an optimal portfolio of projects that defines your data science strategy. This is very similar to what pharmaceutical firms do with their portfolio of R&D drugs.

Next, we work with your human resources department and/or external recruiters to identify the candidates needed to implement this strategy. Our background positions us to identify the right profiles, hence reducing the time you spend to hire this team.

Lastly, we improve collaboration and communication between the business, the data science team and IT, hereby reducing the number of projects that fail. In a world where companies hire almost exclusively specialists, generalists are the missing piece required to bridge the gaps between the different business functions. As such, we speak the language of the business stakeholders, modelers and developers, thanks to our domain knowledge, modeling expertise, development skills and understanding of human behavior.